Introducing 5 New Case Studies

With our expanded mission as The University of Illinois-Deloitte Foundation Center for Business Emerging Technologies, we are hitting the ground running. We are thrilled to showcase five brand-new case studies.

These cases blend foundational business principles with emerging tech tools like generative AI, data simulation, Python, and interactive dashboards.

Students frequently struggle to bridge the gap between understanding analytical concepts and writing the actual code to execute them. This teaching case introduces students to how Large Language Models (LLMs) can be integrated directly into traditional business analytics workflows like data cleaning, exploration, visualization, and predictive modeling.

Instead of treating ChatGPT as a shortcut, students use it to generate, debug, and optimize code while critically comparing their own work to AI-generated solutions. It is an excellent resource for teaching students how to critically assess code trade-offs and navigate the ethical considerations of AI-assisted work.

Using a massive, real-world dataset of purchasing card transactions from the State of Oklahoma, students are put in the shoes of a state consultant. Their task? Determine whether the state should establish a centralized travel management program by weighing the operational costs against potential savings.

Students learn how to clean, categorize, and merge massive tracking files back into a primary dataset. The beauty of this case study is its flexibility: it includes parallel tracks so instructors can teach it entirely within Excel or use it as a hands-on project for Python and Pandas data analysis.

Highland Medical Center’s Pediatrics Unit operates at a fixed 60-bed capacity but found itself turning away patients due to excess demand on 258 days of the year. This advanced case integrates accounting, operations management, and data modeling to solve a complex, real-world healthcare dilemma.

Students apply Cost-Volume-Profit (CVP) analysis to separate fixed and variable costs, model unpredictable patient demand using a Poisson distribution, and run numerical simulations to find the exact bed capacity that maximizes profit. The final twist introduces union-mandated staffing constraints, forcing students to recalculate how labor variables impact overall organizational profitability under uncertainty.

Decision-makers are constantly bombarded with poorly designed, cluttered, or misleading visuals that obscure crucial business data. This project provides a structured learning path to fix that exact problem.

Students explore core visual design principles and build hands-on dashboards using Tableau for professional, interactive presentations, alongside Python to ensure reproducibility and deep analytical exploration. It is a practical course addition designed to equip future business leaders with the ability to turn raw data into a clear, compelling story that drives corporate strategy.

This project introduces students to two common approaches for modeling binary outcomes: the linear probability model and logistic regression. Organizations often seek to predict whether individuals will experience health outcomes that affect healthcare costs and workplace productivity. For example, employers may be interested in predicting whether an employee has high blood pressure. Selecting an appropriate statistical model is important because predictions should be both interpretable and realistic.

Students will work with real-world health screening data from the Illinois Workplace Wellness Study using R. Logistic regression is widely used in practice, but the linear probability model remains popular because of its simplicity and ease of interpretation. Understanding the similarities and differences between these two approaches helps students evaluate the trade-offs between interpretability and predictive accuracy when modeling binary outcomes.